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\subsection{Related Studies on Machine Learning in processing EMG based gestures}  Several studies related to EMG pattern recognition have been conducted over the past decades. These techniques have been used to analyze EMG signals which have been complex to recognize due to large variations in signals. In a study conducted by Liu et al.\cite{Liu_2007}, a novel EMG classifier called cascaded kernel learning machine (CKLM) was proven to be effective, achieving a high recognition rate of 93.54\%. The study employed a cascaded architecture of kernel learning machines including the General Discriminant Analysis (GDA), and the support vector machine (SVM) which offers classification performance that matches or exceeds other classifiers and does so in a computationally efficient manner \cite{Oskoei_2008}.  In a study conducted by Yoshikawa et al. \cite{Yoshikawa_2006}, a real-time hand motion estimation was conducted by using EMG signals with SVMs. The study involved an experiment wherein a subject was required to perform seven hand motions sequentially for 10 sessions. High accuracy is maintained through nine of the sessions, one exhibiting a performance of 94.17\% accuracy.  A similar study of real-time muscle estimation was conducted by Lozito et al. \cite{Lozito_2015}.